Dataset Information

Challenges in glucoCEST MR body imaging at 3 Tesla.

ABSTRACT: Background:The aim of this study was to translate dynamic glucose enhancement (DGE) body magnetic resonance imaging (MRI) based on the glucose chemical exchange saturation transfer (glucoCEST) signal to a 3 T clinical field strength. Methods:An infusion protocol for intravenous (i.v.) glucose was optimised using a hyperglycaemic clamp to maximise the chances of detecting exchange-sensitive MRI signal. Numerical simulations were performed to define the optimum parameters for glucoCEST measurements with consideration to physiological conditions. DGE images were acquired for patients with lymphomas and prostate cancer injected i.v. with 20% glucose. Results:The optimised hyperglycaemic clamp infusion based on the DeFronzo method demonstrated higher efficiency and stability of glucose delivery as compared to manual determination of glucose infusion rates. DGE signal sensitivity was found to be dependent on T2, B1 saturation power and integration range. Our results show that motion correction and B0 field inhomogeneity correction are crucial to avoid mistaking signal changes for a glucose response while field drift is a substantial contributor. However, after B0 field drift correction, no significant glucoCEST signal enhancement was observed in tumour regions of all patients in vivo. Conclusions:Based on our simulated and experimental results, we conclude that glucose-related signal remains elusive at 3 T in body regions, where physiological movements and strong effects of B1 + and B0 render the originally small glucoCEST signal difficult to detect.

SUBMITTER: Kim M

PROVIDER: S-EPMC6828585 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Challenges in glucoCEST MR body imaging at 3 Tesla.

Kim Mina M Torrealdea Francisco F Adeleke Sola S Rega Marilena M Evans Vincent V Beeston Teresita T Soteriou Katerina K Thust Stefanie S Kujawa Aaron A Okuchi Sachi S Isaac Elizabeth E Piga Wivijin W Lambert Jonathan R JR Afaq Asim A Demetriou Eleni E Choudhary Pratik P Cheung King Kenneth KK Naik Sarita S Atkinson David D Punwani Shonit S Golay Xavier X

Quantitative imaging in medicine and surgery 20191001 10

<h4>Background</h4>The aim of this study was to translate dynamic glucose enhancement (DGE) body magnetic resonance imaging (MRI) based on the glucose chemical exchange saturation transfer (glucoCEST) signal to a 3 T clinical field strength.<h4>Methods</h4>An infusion protocol for intravenous (i.v.) glucose was optimised using a hyperglycaemic clamp to maximise the chances of detecting exchange-sensitive MRI signal. Numerical simulations were performed to define the optimum parameters for glucoC ...[more]

PMID: 31728307

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Project description:PurposeEpilepsy patients exhibit morphological differences on neuroimaging compared to age-matched healthy controls, including cortical and sub-cortical volume loss and altered gray-white matter ratios. The objective was to develop a model of normal aging using the 7T MRIs of healthy controls. This model can then be used to determine if the changes in epilepsy patients resemble the changes seen in aging, and potentially give a marker for the severity of those changes.MethodsSixty-nine healthy controls (24F/45M, mean age 36.5 ± 10.5 years) and forty-four epilepsy patients (24F/20M, 33.2 ± 9.9 years) non-lesional at 3T were scanned with volumetric T1-MPRAGE at 7T. These images were segmented and quantified using FreeSurfer. A linear regression-based model trained on healthy controls was developed to predict ages using derived imaging features among the epilepsy patient cohort. The model used 114 features with significant linear correlation with age.ResultsThe regression-based model estimated brain age with mean absolute error (MAE) of 6.6 years among controls. Comparable prediction accuracy of 6.9 years MAE was seen epilepsy patients. T-test of mean absolute error showed no difference in the prediction accuracy with controls and epilepsy patients (p = 0.68). However, average signed error showed elevated (+5.0 years, p = 0.0007) predicted age differences (PAD; brain-PAD=, predicted minus biological age) among epilepsy patients. Morphological metrics in the medial temporal lobe were major contributors to PAD. Additionally, patients with seizure frequency greater than once a week showed significantly elevated brain-PAD (+8.2 ± 5.3 years, n = 13) compared to patients with lower seizure frequency (3.7 ± 6.5 years, n = 31, p = 0.033).Major conclusionsMorphological patterns suggestive of premature aging were observed in non-lesional epilepsy patients vs. controls and in high seizure frequency patients vs. low frequency patients. Modeling brain age with 7T MRI may provide a sensitive imaging marker to assess the differential effects of the aging process in diseases such as epilepsy.

Dataset Information

Challenges in glucoCEST MR body imaging at 3 Tesla.

Publications

Challenges in glucoCEST MR body imaging at 3 Tesla.

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